Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU

• URL: http://arxiv.org/abs/2203.05947v1
• Date: Fri, 11 Mar 2022 14:36:52 GMT
• Title: Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU
• Authors: Hollan Haule, Evangelos Kafantaris, Tsz-Yan Milly Lo, Chen Qin, Javier Escudero
• Abstract summary: This paper investigates the utilization of a hybrid artifact detection system that combines a Variational Autoencoder with a statistical detection component for the labeling of artifactual samples. Our preliminary results indicate that the system is capable of consistently achieving sensitivity and specificity levels that surpass 90%.
• Score: 1.8374319565577155
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Physiological monitoring in intensive care units generates data that can be used to aid clinical decision making facilitating early interventions. However, the low data quality of physiological signals due to the recording conditions in clinical settings limits the automated extraction of relevant information and leads to significant numbers of false alarms. This paper investigates the utilization of a hybrid artifact detection system that combines a Variational Autoencoder with a statistical detection component for the labeling of artifactual samples to automate the costly process of cleaning physiological recordings. The system is applied to mean blood pressure signals from an intensive care unit dataset recorded within the scope of the KidsBrainIT project. Its performance is benchmarked to manual annotations made by trained researchers. Our preliminary results indicate that the system is capable of consistently achieving sensitivity and specificity levels that surpass 90%. Thus, it provides an initial foundation that can be expanded upon to partially automate data cleaning in offline applications and reduce false alarms in online applications.

Related papers

• Noninvasive Acute Compartment Syndrome Diagnosis Using Random Forest Machine Learning [0.0]
  Acute compartment syndrome (ACS) is an orthopedic emergency, caused by elevated pressure within a muscle compartment. This study proposes an objective and noninvasive diagnostic for ACS. The device detects ACS through a random forest machine learning model that uses surrogate pressure readings from force-sensitive resistors (FSRs) placed on the skin.
  arXiv  Detail & Related papers  (2024-01-18T21:49:04Z)
• Multimodal Pretraining of Medical Time Series and Notes [45.89025874396911]
  Deep learning models show promise in extracting meaningful patterns, but they require extensive labeled data. We propose a novel approach employing self-supervised pretraining, focusing on the alignment of clinical measurements and notes. In downstream tasks, including in-hospital mortality prediction and phenotyping, our model outperforms baselines in settings where only a fraction of the data is labeled.
  arXiv  Detail & Related papers  (2023-12-11T21:53:40Z)
• VAE-IF: Deep feature extraction with averaging for fully unsupervised artifact detection in routinely acquired ICU time-series [1.9665926763554147]
  We propose a novel fully unsupervised approach to detect artifacts in minute-by-minute resolution ICU data without prior labeling or signal-specific knowledge. Our approach combines a variational autoencoder (VAE) and an isolation forest (IF) into a hybrid model to learn features and identify anomalies. We show that our unsupervised approach achieves comparable sensitivity to fully supervised methods and generalizes well to an external dataset.
  arXiv  Detail & Related papers  (2023-12-10T18:03:40Z)
• Deep Scattering Spectrum germaneness to Fault Detection and Diagnosis for Component-level Prognostics and Health Management (PHM) [0.0]
  This work focuses on the study of the Deep Scattering Spectrum (DSS)'s relevance to fault detection and daignosis for mechanical components of industrail robots. We used multiple industrial robots and distinct mechanical faults to build an approach for classifying the faults. The presented approach was implemented on the practical test benches and demonstrated satisfactory performance in fault detection and diagnosis.
  arXiv  Detail & Related papers  (2022-10-18T13:25:02Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals [77.34726150561087]
  We propose a methodology based on deep metric learning to address the need for automated post-hoc cleaning and robust separation filters. We validate this method with an artificially corrupted label set based on source-separated high-density surface electromyography recordings. This approach enables a neural network to learn to accurately decode neurophysiological time series using any imperfect method of labelling the signal.
  arXiv  Detail & Related papers  (2021-10-13T21:51:56Z)
• Multiple Organ Failure Prediction with Classifier-Guided Generative Adversarial Imputation Networks [4.040013871160853]
  Multiple organ failure (MOF) is a severe syndrome with a high mortality rate among Intensive Care Unit (ICU) patients. Applying machine learning models to electronic health records is a challenge due to the pervasiveness of missing values.
  arXiv  Detail & Related papers  (2021-06-22T15:49:01Z)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.